Calibration techniques for imaging devices

ABSTRACT

The invention is directed to various calibration techniques for calibrating an imagining device such as a display device, a printer, or a scanner. The techniques may involve characterizing the imaging device with a device model such that an average error between expected outputs determined from the device model and measured outputs of the imaging device is on the order of an expected error, and adjusting image rendering on the imaging device to achieve a target behavior. The invention can achieve a balance between analytical behavior of the imaging device and measured output. In this manner, adjustments to image rendering may be more likely to improve color accuracy and less likely to overcompensate for errors that are expected.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a Continuation of prior U.S. patent application Ser. No.10/854,113, filed May 26, 2004, which is a Divisional of U.S. patentapplication Ser. No. 10/039,669, filed Dec. 31, 2001, now issued as U.S.Pat. No. 6,775,633, issued Aug. 10, 2004, all of which documents citedin this section are hereby incorporated in their entirety herein byreference.

FIELD OF THE INVENTION

The invention relates to color imaging and, more particularly, tocalibration techniques for color imaging devices.

BACKGROUND OF THE INVENTION

Calibration of an imaging device can significantly improve coloraccuracy of images rendered by the device. For example, imaging devicessuch as cathode ray tubes, liquid crystal displays, plasma displays, andvarious printing devices, are often calibrated to determine adjustmentsthat can be applied to either color input or drive data applied to thedevice. In either case, the adjusted data can then be used to controlthe imaging device such that the ultimate rendition of the image hasimproved color accuracy. Calibration can be used to account for drift inthe imaging device to improve color accuracy.

For example, the calibration of a cathode ray tube (CRT) may involveattaching a measurement device, such as a calorimeter, to the displayscreen to measure color output of the CRT. The measured output can thenbe compared to analytical expected color values to determine the colorerrors. The determined errors can then be used to modify a lookup table(LUT) in a video card associated with a host computer so that inputcolor data can be converted in a manner that adjusts for the determinederrors. The effectiveness and accuracy of the calibration process cansubstantially impact color accuracy.

Precise color accuracy is particularly important for color intensiveapplications such as soft proofing. Soft proofing refers to a proofingprocess that makes use of a display device rather than a printed hardcopy. Traditionally, color proofing techniques have relied on hard copyproofing, where proofs are printed and inspected in order to ensure thatthe images and colors on the print media look visually correct. Forinstance, color characteristics can be adjusted and successive hard copyprints can be examined in a hard proofing process. After determiningthat a particular proof is acceptable, the color characteristics used tomake the acceptable proof can be reused to mass-produce, e.g., on aprinting press, large quantities of print media that look visuallyequivalent to the acceptable proof.

Soft proofing is desirable for many reasons. For instance, soft proofingcan eliminate or reduce the need to print hard copies on media duringthe proofing process. Moreover, soft proofing may allow multipleproofing specialists to proof color images from remote locations simplyby looking at display devices. With soft proofing, there is no need toprint and deliver hard copy proofs to remote reviewers. Thus, softproofing can be faster and more convenient than hard copy proofing.Moreover, soft proofing can reduce the cost of the proofing process. Forthese and other reasons, soft proofing is highly desirable. The abilityto achieve precise calibration of soft proofing display devices is animportant factor to achieving an effective soft proofing system.

SUMMARY OF THE INVENTION

In general, the invention is directed to various calibration techniquesfor calibrating an imaging device such as a display device, a printer ora scanner. The techniques may involve characterizing the imaging devicewith a device model, wherein an average error between an expected valueof the device model and measured output of the image device is on theorder of an expected error. The invention can achieve a balance betweenanalytical behavior of the imaging device and measured output. In thismanner, adjustments to image data may be more likely to improve coloraccuracy and less likely to overcompensate for errors that are expected.

In various embodiments, the invention may be directed to methods ofcalibrating an imaging device. For example, a method may includecharacterizing the imaging device with a device model such that anaverage error between expected outputs determined from the device modeland measured outputs of the imaging device is on the order of anexpected error. The method may also include adjusting image rendering onthe imaging device to achieve a target behavior.

In another embodiment, the invention may be directed to a method thatincludes measuring outputs of the cathode ray tube for a subset ofdevice values of the cathode ray tube, and choosing one or moreparameter values of a device model, wherein the number of adjustableparameters is less than a number of measurements used to define themeasured output of the cathode ray tube, and wherein an average errorbetween expected outputs of the device model and the measured outputs ison the order of an expected error. The method may further includeadjusting image data according to the device model to achieve a targetbehavior for the imaging device.

In another embodiment the invention may be directed to a method thatincludes initializing a lookup table (LUT), adjusting settings of thecathode ray tube to substantially achieve a defined output, andmeasuring output for a number of color values. The method may alsoinclude choosing parameter values for a device model, wherein a numberof adjustable parameters is less than a number of measured outputs, andgenerating entries for the LUT based on the device model.

In another embodiment, the invention may implement a technique forbiasing an output measurement by an amount sufficient to ensure that theoutput measurement is within a dynamic range of a measurement device.For example, a method may include measuring output of a display device,and displaying a substantially white trace during measurement to biasthe output measurement. The trace may have a halo shape, or any othershape sufficient to properly bias the measurements.

In other embodiments, the invention is directed to calibrated imagingdevices or sets of calibrated imaging devices. For example, inaccordance with the invention, a cathode ray tube, or a set of cathoderay tubes can be calibrated such that an average color error isapproximately less than (0.75 delta e) from an analytical expected coloroutput, and a maximum color error is approximately less than (1.5 deltae) from the analytical expected color output. Furthermore, even moreprecise calibration, approaching a theoretical limits of analyticequations used to define device behavior can be achieved as described ingreater detail below.

Various aspects of the invention may be implemented in hardware,software, firmware, or any combination thereof. If implemented insoftware, the invention may be directed to a computer readable mediumcarrying program code, that when executed, performs one or more of themethods described herein.

The invention is capable of providing a number of advantages. Inparticular, the invention can improve calibration of imaging devices.Moreover, improved calibration can facilitate the realization of colorintensive applications such as soft proofing. In some cases, theinvention can be used to calibrate imaging devices such that measurederrors of the imaging device are on the order of expected errors. Forexample, expected errors in the measurements may be caused by factorsunrelated to the imaging device, such as errors introduced by themeasuring device or the video card. The invention can achieve a balancebetween theory and measurement to ensure that adjustments to image datado not overcompensate for measured errors unrelated to the imagingdevice itself. In this manner, an imaging device can be calibrated suchthat an average color error is approximately less than (0.75 delta e)from an analytical expected color output.

Additional details of these and other embodiments are set forth in theaccompanying drawings and the description below. Other features, objectsand advantages will become apparent from the description and drawings,and from the claims

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a front view of an exemplary imaging station including acathode ray tube calibrated according to the invention.

FIG. 2 is a functional block diagram of an exemplary implementation ofan imaging station according to the invention.

FIG. 3 is a flow diagram illustrating a calibration technique accordingto the invention.

FIG. 4 illustrates one example of an imaging station displaying a whitetrace during calibration.

FIGS. 5 and 6 are additional flow diagrams illustrating calibrationtechniques according to the invention.

FIG. 7 is another functional block diagram of an exemplaryimplementation of an imaging station according to the invention.

FIG. 8 is a block diagram of an exemplary soft proofing system includinga number of imaging stations implementing the invention.

DETAILED DESCRIPTION OF THE INVENTION

In the discussion that follows, many aspects of the invention aredescribed with reference to the calibration of an imaging device in theform of a cathode ray tube (CRT). However the invention is notnecessarily limited in that respect. For example, techniques accordingto the principles of the invention may be readily applicable to otherimaging devices, including other display devices such as liquid crystaldisplays, plasma displays, projection displays, and the like; printingdevices such as printing presses, laser printers, ink-jet printers,dot-matrix printers, or any other printing device; and other imagingdevices such as scanners. Accordingly, the detailed discussion is meantto be an exemplary description of one detailed embodiment in accordancewith the invention.

FIG. 1 is a front view of an exemplary imaging station 10. Imagingstation 10 includes an imaging device in the form of a cathode ray tube(CRT) 12. In addition, imaging station 10 includes a computer 14 thatcan receive image data and drive CRT 12 according to the received imagedata. Imaging station 10 utilizes one or more of the calibrationtechniques described herein to improve rendering of color imagery on CRT12. In accordance with the invention, CRT 12 can be calibrated such thatan average color error is approximately less than (0.75 delta e) from ananalytical expected color output, and a maximum color error isapproximately less than (1.5 delta e) from the analytical expected coloroutput. More specifically, in some cases CRT 12 can be calibrated suchthat an average color error is approximately between (0.3 delta e) and(0.75 delta e) from the analytical expected color output, or evenapproximately between (0.3 delta e) and (0.4 delta e) from theanalytical expected color output. In that case, the maximum color errormay be approximately between (0.6 delta e) and (1.1 delta e) from theanalytical expected color output, or even approximately between (0.6delta e) and (0.8 delta e) from the analytical expected color output. Inother words, the invention can facilitate calibration accuracy of animaging device that approaches a theoretical limit.

Computer 14 may substantially conform to conventional computers used bygraphic artists and other users in the creation of graphic imagery forelectronic display or print production. For example, computer 14 mayinclude a processor, memory, and an external storage device. Amemory/bus controller and system bus typically couple the processor andmemory, while one or more I/O controllers and an I/O bus couple theprocessor and memory to the storage device and CRT 12. Computer 14 mayalso include a user input device coupled to the processor an memory viaan I/O bus.

The processor of computer 14 may take the form of a general purposemicroprocessor and can be integrated with or form part of a PC,Macintosh, computer workstation, or the like. The user input device ofcomputer 14 may include a conventional keyboard and pointing device suchas a mouse, pen, or trackball, if desired. The memory of computer 14 mayinclude random access memory (RAM) storing program code that is accessedand executed by the processor to carry out the calibration techniquesdescribed below.

For example, program code implementing calibration techniques accordingto the invention can be loaded into the memory of computer 14 from theexternal storage device of computer 14, which may take the form of afixed hard drive or removable media drive. The program code can beinitially carried on computer-readable media such as magnetic, optical,magneto-optic or other disk or tape media. Alternatively, the programcode may be loaded into memory from electronic computer-readable mediasuch as electrically-erasable-programmable-read-only-memory (EEPROM), ordownloaded over a network connection. If downloaded, the program codemay be initially embedded in a carrier wave or otherwise transmitted onan electromagnetic signal. The program code may be embodied as a featurein an application program providing a wide range of imagingfunctionality.

FIG. 2 is functional block diagram of an exemplary implementation of aimaging station 10 according to the invention. The various functionalblocks may be implemented in hardware, or may be implemented in softwarewhich is executed in a processor within computer 14 as mentioned above.

Imaging station 10 receives red-green-blue (RGB) image data as indicatedat reference numeral 21. Upon receiving RGB image data, the data can bemanipulated by color matching module 22. In particular, color matchingmodule may convert the RGB data using a color profile associated withthe specific make and model of CRT 12. The converted data can then besent through display driver 25 and video card 26 to ultimately drive thepixels of CRT 12 in a manner that yields a very accurate rendition ofcolor images.

To further improve color accuracy, imaging station 10 includes acalibration module 24 to calibrate CRT 12 in order to account for suchthings as drift in CRT 12. Calibration module 24 can be invoked tomeasure output of CRT 12, and then, adjustments can be loaded into LUT29 within video card 26. Video card 26 accesses LUT 29 to convert imagevalues into drive values to be applied to CRT 12. These additionaladjustments can be applied to the image data so that the ultimaterendition of CRT 12 has more accurate color.

Calibration module 24 may invoke the calibration techniques described ingreater detail below in order to achieve improved color accuracy.Typically, when a calibration technique is invoked, calibration module24 prompts a user to attach a color measurement device to CRT 12. Forexample, the color measurement device is typically a high quality lightdetector that can be affixed to the display screen of CRT 12. Devicenumber DTP92, commercially available from X-Rite Incorporated of GrandRapids, Mich. is one suitable measurement device. After, the colormeasurement device has been affixed to the display screen of CRT 12, auser can initiate the calibration process. In that case, calibrationmodule 24 performs a number of measurements on the output of CRT 12.After measuring the output of CRT 12, calibration module 24 can define adevice model of CRT, and use the device model to generate adjustedvalues that can be loaded into LUT 29. In this manner, the calibrationprocedure can account for drift in CRT 12 and thereby improve coloraccuracy of images rendered on CRT 12.

Calibration module 24 may begin with a presumption that CRT 12 iscalibrated to a reference RGB color space having a perfect gamma curvebehavior:

$\begin{matrix}{{\begin{pmatrix}X_{1} \\Y_{1} \\Z_{1}\end{pmatrix} = {\begin{bmatrix}X_{r\; 1} & X_{g\; 1} & X_{b\; 1} \\Y_{r\; 1} & Y_{g\; 1} & Y_{b\; 1} \\Z_{r\; 1} & Z_{g\; 1} & Z_{b\; 1}\end{bmatrix}\begin{pmatrix}{f_{r\; 1}(R)} \\{f_{g\; 1}(G)} \\{f_{b\; 1}(B)}\end{pmatrix}}}{where}} & {{EQUATION}\mspace{14mu} 1} \\{{f_{r\; 1}(R)} = R^{\gamma \; 1}} & {{EQUATION}\mspace{14mu} 2A} \\{{f_{g\; 1}(G)} = G^{\gamma \; 1}} & {{EQUATION}\mspace{14mu} 2B} \\{{f_{b\; 1}(B)} = B^{\gamma \; 1}} & {{EQUATION}\mspace{14mu} 2C}\end{matrix}$

EQUATIONS 1, 2A, 2B and 2C have normalized RGB values between 0.0 and1.0. However, display values captured in the calibration processtypically have 0-255 gray level values.

A gamma value can be chosen as a standard target. For example, a gammavalue of 2.2 is often assumed for uncorrected CRT displays and isreasonably linear with respect to L*, i.e., in the L*a*b* color space.As described in greater detail below, calibration module 24 mayimplement techniques that can match this gamma behavior approximately towithin an expected error.

The matrix of EQUATION 1 can be redefined using standard values ofchromaticity for R, G and B and for Yxy (luminance and chromaticity) ofthe white point of CRT 12. The matrix M for converting RGB to XYZ can bedefined according to the chromaticities of the RGB values as follows:

$\begin{matrix}{{{M\begin{pmatrix}{x_{r\; 1},y_{r\; 1},x_{g\; 1},y_{g\; 1},} \\{{x_{b\; 1}y_{b\; 1}},x_{wp},y_{wp}}\end{pmatrix}} = {{M_{c}\begin{pmatrix}{x_{r\; 1},y_{r\; 1},x_{g\; 1},} \\{y_{g\; 1},{x_{b\; 1}y_{b\; 1}}}\end{pmatrix}}\begin{pmatrix}{Y_{r\; 1}\left( {x_{wp},y_{wp}} \right)} & 0 & 0 \\0 & {Y_{g\; 1}\left( {x_{wp},y_{wp}} \right)} & 0 \\0 & 0 & {Y_{b\; 1}\left( {x_{wp},y_{wp}} \right)}\end{pmatrix}}}{where}} & {{EQUATION}\mspace{14mu} 3} \\{{{M_{c}\begin{pmatrix}{x_{r\; 1},y_{r\; 1},x_{g\; 1},} \\{y_{g\; 1},{x_{b\; 1}y_{b\; 1}}}\end{pmatrix}} = \begin{pmatrix}{x_{r\; 1}\text{/}y_{r\; 1}} & {x_{g\; 1}\text{/}y_{g\; 1}} & {x_{b\; 1}\text{/}y_{g\; 1}} \\1 & 1 & 1 \\{\left( {1 - x_{r\; 1} - y_{r\; 1}} \right)\text{/}y_{r\; 1}} & {\left( {1 - x_{g\; 1} - y_{g\; 1}} \right)\text{/}y_{g\; 1}} & {\left( {1 - x_{b\; 1} - y_{b\; 1}} \right)\text{/}y_{b\; 1}}\end{pmatrix}}{and}} & {{EQUATION}\mspace{14mu} 4} \\{\begin{pmatrix}{Y_{r\; 1}\left( {x_{wp},y_{wp}} \right)} \\{Y_{g\; 1}\left( {x_{wp},y_{wp}} \right)} \\{Y_{b\; 1}\left( {x_{wp},y_{wp}} \right)}\end{pmatrix} = {M_{c}^{- 1}\begin{pmatrix}{x_{wp}\text{/}y_{wp}} \\1 \\{\left( {1 - x_{wp} - y_{wp}} \right)\text{/}y_{wp}}\end{pmatrix}}} & {{EQUATION}\mspace{14mu} 5}\end{matrix}$

In order to achieve a particular targeted luminance for white Y_(wp), inunits of candelas/meter², the matrix M can be multiplied by Y_(wp).Thus, EQUATION 1 can be rewritten as:

$\begin{matrix}{\begin{pmatrix}X_{1} \\Y_{1} \\Z_{1}\end{pmatrix} = {Y_{{WP}\; 1}{M\left( {X_{{WP}\; 1},Y_{{WP}\; 1}} \right)}\begin{pmatrix}{f_{r\; 1}(R)} \\{f_{r\; 1}(G)} \\{f_{r\; 1}(B)}\end{pmatrix}}} & {{EQUATION}\mspace{14mu} 6}\end{matrix}$

where the values of RGB chromaticities are assumed to be constants.

Experimental data gathered on several dozen CRTs, ranging in age fromzero to eight years old, has indicated that the RGB chromaticities tendto remain very constant over time and between brands of CRTs. Thus, thevariables that require adjustment are primarily the Yxy white point andthe RGB gamma curves of the CRT. However, more complex adjustments whichinclude changes in RGB chromaticities can also be performed as desired.

The measured behavior of an uncalibrated CRT can be defined as follows:

$\begin{matrix}{{\begin{pmatrix}X \\Y \\Z\end{pmatrix} = {\begin{pmatrix}X_{dc} \\Y_{dc} \\Z_{dc}\end{pmatrix} + {Y_{{wp}\; 2}{M\left( {X_{{wp}\; 2},Y_{{wp}\; 2}} \right)}\begin{pmatrix}{f_{r\; 2}(R)} \\{f_{g\; 2}(G)} \\{f_{b\; 2}(B)}\end{pmatrix}}}}{where}} & {{EQUATION}\mspace{14mu} 7} \\{{f_{r\; 2}(R)} = \left\lbrack \frac{\left( {R - R_{o\; 2}} \right)}{\left( {1.0 - R_{o\; 2}} \right)} \right\rbrack^{\gamma_{r\; 2}}} & {{EQUATION}\mspace{14mu} 8A} \\{{f_{g\; 2}(G)} = \left\lbrack \frac{\left( {G - G_{o\; 2}} \right)}{\left( {1.0 - G_{o\; 2}} \right)} \right\rbrack^{\gamma_{r\; 2}}} & {{EQUATION}\mspace{14mu} 8B} \\{{f_{b\; 2}(R)} = \left\lbrack \frac{\left( {B - B_{o\; 2}} \right)}{\left( {1.0 - B_{o\; 2}} \right)} \right\rbrack^{\gamma \; r\; 2}} & {{EQUATION}\mspace{14mu} 8C}\end{matrix}$

The vector XYZ_(dc) is the dark current offset introduced by themeasurement device plus stray light. Typical values for the X-Rite DTP92measurement device are on the order of 0.3 candelas/meter², comparedwith the white point luminosity of 81.5 candelas/meter².

The values of RGB_(o2) represent the black onsets, which indicate thethreshold values of RGB above which an increase in XYZ can be detected.For perfect CRT behavior (as represented in EQUATION 1) the values ofRGB_(o2) are zero.

The vector XYZ_(dc) may by a constant or may vary as a function of(1.0−α) where:

α=(XYZ(RGB)−XYZ(RGB=0))/(XYZ(RGB=255)−XYZ(RGB=0))  EQUATION 9

For example, for some measurement devices, the value of XYZ for RGB=0can vary significantly, while the value of XYZ for RGB=255 is verystable and repeatable. Thus, for some measurement devices the value ofXYZ_(dc) can be multiplied by (1.0−α).

FIG. 3 is a flow diagram illustrating a calibration technique accordingto an embodiment of the invention. As shown, calibration module 24initializes LUT 29 within video card 26 (31). In other words, LUT 29 isset to linear in order to ensure that the output of CRT 12 is in a knownstate. Exemplary pseudo code for the initialization is provided below.

#define numEntries 1024 #define max16bit 0xffff unsigned short videoLUT[numEntries]; int i; (for i=0; i< numEntries;i++)  videoLUT[i] =(unsigned short)(0.5 + max16bit *  ((float)i/(float)(numEntries−1));

Although this linear video LUT will be altered during the calibrationprocedure, it is advantageous to ensure that videoLUT[0] is always keptat 0. This will ensure that the measured value of RGB=0 is a constantthroughout the calibration process.

After the LUT has been initialized (31), the settings of CRT 12 can beadjusted (32), either automatically by calibration module 24 or manuallyby a user. Adjusting the analog settings of CRT 12 further ensures thatthe initial state of CRT 12 is known and defined. As mentioned above,the state of the RGB target space can be defined by white point andgamma values for RGB, assuming the RGB chromaticities are constant. Thevalues of the analog settings are typically referred to by the followingnames. Contrast refers to the setting that adjusts primarily theluminance of the near white colors for the CRT (which affect RGB valuestogether), and secondarily affect dark luminance values. Brightnessrefers to the setting that adjusts primarily the luminance of the darkluminance colors for the CRT (which affect RGB values together), andsecondarily affects near white values. Gain refers to the setting thatadjusts similar values as contrast, but does so separately for each R,G, and B channel. Bias refers to the setting that adjusts similar valuesas brightness, but does so separately for each R, G, and B channel.

The above analog settings can be set either manually, e.g., using inputbuttons, dials and the like presented by CRT 12, or automatically viadirect communication between computer 14 and CRT 12. An example of suchdirect communication is the Video Electronics Standards Association(VESA®) standard known as ddc/ci. In one specific implementation, thevalues of contrast, brightness, and the gains and biases of R, G, and Bcan be adjusted to achieve the following measured behavior:

-   -   1) The value of Yxy for white (RGB=255) is set as close as        possible to the target values of Yxy with the exception that the        value of Y is set slightly (i.e. 1%) higher than the value of Y        for the white of the RGB target space. This allows room to        perform remaining corrections via LUT 29 in video card 26. The        target value of Yxy for white can be chosen by the user        according to the preferences or needs of the user.    -   2) The value of x,y for a dark gray (e.g. RGB=50) is set as        close as possible to the value of x,y for the target white.        Again, how the target is defined may depend on the preferences        of the user.    -   3) The luminosity Y of the dark gray can be adjusted in one of        two ways that are essentially equivalent. In one case, the dark        gray luminosity is set such that the value of brightness is as        high as possible while still measuring perfect black at RGB=0.        “Perfect black” is defined as the value of Y for the CRT with        RGB=0, and all analog adjustment values set to minimum. In the        other case, the highest value of brightness is determined which        results in a value of the black onsets of RGB_(o2)=0 or slightly        above 0.

For some CRT's, the gamma value is approximately 2.35. In that case, ithas been found that the desired value of brightness occurs whenL*=13.0−14.0 at RGB=48. The relative values of RGB bias can be adjustedto measure a*=b*=0. In that case, all calculations for CIELAB assumethat XnYnZn is determined by the value Yxy of the white point of the RGBtarget for CRT 12.

After adjusting the settings (32), output of CRT 12 is measured (33).For example, calibration module 24 may direct the user to attach acalibration device to the screen of the CRT to measure output. When themeasurement device is attached, calibration module 24 can perform acalibration routine that involves displaying and measuring colorsamples. For example, data capturing may involve displaying andmeasuring RGB gray values ranging from 0 to 255. All of the gray levelscan be measured, or alternatively, a subset of gray levels can bemeasured in order to reduce the time it takes to capture data.Measurements in 15 gray level increments can provide a suitable level ofaccuracy. Each gray level defines a unique “neutral color.” Neutralcolors refer to colors having substantially equivalent color values. Forexample, neutral colors for a CRT are colors having substantiallyequivalent values for the R, G, and B channels.

The calibration routine may display a circle representing RGB neutralcolor (typically a circle with a ten centimeter diameter or larger)against a black (RGB=0) background. The black background can helpimprove the ability to measure the darker colors. A number of XYZ valuescan be captured for each RGB value that is displayed in order to obtainan estimate of measurement variability, i.e., an error value (sigma)associated with the measurement device. The calibration routine can thenproceed to display the next RGB value and repeat the process until allRGB values, or a subset thereof, have been captured.

One concern in measurement of dark emissive colors is that somemeasurement devices clip the data. In other words, for some measurementdevices, values of X, Y, or Z in the range of RGB=0 to RGB=40 may bereturned as 0 when a truly accurate value would be finite but small,e.g. 0.4 candelas/meters². To compensate for clipping in the measurementdevice, it may be advantageous to display a substantially white traceduring the calibration routine to slightly bias the measurement device.The white trace may be a fixed white circle (or halo) having colorvalues of RGB=255. The scattered light from this halo is a constantnon-zero quantity and can offer the beneficial effect of adding a slightbias to the values of XYZ measured in darker colors. Hence, all valuesof RGB from 0 to 255 measured in this manner can be guaranteed to bewithin the dynamic range capabilities of the measurement device.

FIG. 4 illustrates one example of an imaging station 10 displaying awhite trace during calibration. Measurement device 42 can be affixed tothe display screen of CRT 12, and substantially white trace 44 can bedisplayed during the calibration procedure to bias the measurements inorder to ensure that the values of RGB from 0 to 255 are within thedynamic range capabilities of measurement device 42. The color beingmeasured may fill the interior of white trace 44, or alternatively boththe color being measured and a black background may be included in theinterior of white trace 44. In either case, white trace 44 can be usedto provide a desired amount of biasing. For example, biasing the outputmeasurements by approximately 0.5 to 1.0 percent is usually sufficient.However, depending on the measurement device, a larger amount of biasmay be useful. White trace 44 is illustrated as having a halo shape, butcould be implemented with any shape or design. Also, biasing with othercolors may be useful for some measurement devices.

After the output has been properly measured (33), the parameter valuesof the device model are chosen (34). The number of adjustable parameterscan be defined in the device model to be less than a number ofmeasurements used to define the measured output of cathode ray tube 12.By defining the number of parameters to be less than, or substantiallyless then the number of measurements used to define the measured output,it can be ensured that the calibration process will not overcompensatefor measured results. In other words, there is always some level oferror that can be attributed to the measurement device or other externalfactors. The invention can avoid improperly compensating for externalfactors unrelated to the actual device output by limiting the number ofadjustable parameters to be less than, or substantially less then thenumber of measurements used to define the measured output. Specifically,the invention can avoid adjusting each input according to the measuredoutput for that given input. Rather, the invention may implement adevice model based partially in theory and partially on chosen parametervalues. In this manner, adjustments do not overcompensate for measurederrors that should be expected. Values for the adjustable parameters canbe chosen during the calibration process in a manner that minimizes theerror between analytical expected outputs and measured outputs.

Which parameters are defined as adjustable may depend on theimplementation. Indeed, for different imaging devices, the adjustableparameters would typically be different. However, in the example ofcalibrating CRT 12, the adjustable parameters may be a gamma value and ablack onset value. The gamma value refers to the value of a well knowparameter in the art (γ) that indicates the rate of change in lightintensity with change in digital device value. The black onset value isanother well know term in the art, which refers to the point at whichthere is a measurable increase in light.

The parameter values of the device model can be chosen by an errorminimization process in order to define a robust device model that isaccurate to an extent approaching theoretical limits. In one example,the device model of a CRT is defined using the following process. First,the captured XYZ data is adjusted with respect to a dark current bias.In particular, the dark current bias (XYZ_(dc) defined above) of the XYZdata can be subtracted from the raw measured XYZ data. If the value ofXYZ_(dc) is a constant for a particular measurement device or method ofmeasurement, then the value of XYZ_(dc) can simply be subtracted fromall values of XYZ. However, if the value of XYZ_(dc) varies according tothe XYZ value, the adjustment to the XYZ data should be performedaccordingly. The result of this adjustment to the XYZ data is that thevalue of XYZ_(dc) in EQUATION 7 is zero. Thus, only the remainingparameters must be determined in order to accurately characterize theCRT behavior.

Next, the white point values are set. For example, the values of(Yxy)_(wp2) can be plugged into EQUATION 7 above. In that case, for(Yxy)_(wp2), the values of XYZ_(wp2) are given by the XYZ data atRGB=255, and the definitions for x and y are:

x=X/(X+Y+Z)  EQUATION 10A

y=Y/(X+Y+Z)  EQUATION 10B

Next, an error function can be defined that depends on the chosenparameter values, in this case the gamma and black onset parametervalues. For example, an error function can be defined as follows:

$\begin{matrix}{{{{Error}\left( {\gamma_{r\; 2},\gamma_{g\; 2},\gamma_{b\; 2},{R\; G\; B}} \right)} = {\sum\limits_{i = 0}^{i = {N - 1}}{\Delta \; {E_{i}^{2}\left( {\gamma_{r\; 2},\gamma_{g\; 2},\gamma_{b\; 2},{R\; G\; B_{o\; 2}}} \right)}}}}\mspace{79mu} {where}} & {{EQUATION}\mspace{14mu} 11} \\{{\Delta \; {E_{i}^{2}\left( {\gamma_{r\; 2},\gamma_{g\; 2},\gamma_{b\; 2},{R\; G\; B_{o\; 2}}} \right)}} = {\left( {{L^{*}\left( {{R\; G\; B_{i}\gamma_{r\; 2}},\gamma_{g\; 2},\gamma_{b\; 2},{R\; G\; B_{o\; 2}}} \right)} - L_{i}^{*}} \right)^{2} + \left( {{a^{*}\left( {{R\; G\; B_{i}},\gamma_{r\; 2},\gamma_{g\; 2},\gamma_{b\; 2},{R\; G\; B_{o\; 2}}} \right)} - a_{i}^{*}} \right)^{2} + \left( {{b^{*}\left( {{R\; G\; B_{i}},\gamma_{r\; 2},\gamma_{g\; 2},\gamma_{b\; 2},{R\; G\; B_{o\; 2}}} \right)} - b_{i}^{*}} \right)^{2}}} & {{EQUATION}\mspace{14mu} 12}\end{matrix}$

L*( ), a*( ) and b*( ) are the analytical expected values of L*a*b*,whereas the valuesL*_(i), a*_(i), b*_(i) are the measured values of L*a*b*. The analyticalexpected values and measured CIELAB values can be derived fromanalytical and measured XYZ. The analytical expression for X,Y, and Z,as a function of RGB, XYZ_(dc), γ_(r2), γ_(g2), γ_(b2), RGB_(o2), isdefined in EQUATION 6 and EQUATION 7 above. The calculation for CIELABvalues from XYZ is given by:

$\begin{matrix}{L^{*} = {{116{f\left( {Y\text{/}Y_{n}} \right)}} - 16}} & {{EQUATION}\mspace{14mu} 13A} \\{a^{*} = {500\left\lbrack {{f\left( {X\text{/}X_{n}} \right)} - {f\left( {Y\text{/}Y_{n}} \right)}} \right\rbrack}} & {{EQUATION}\mspace{14mu} 13B} \\{{b^{*} = {200\left\lbrack {{f\left( {Y\text{/}Y_{n}} \right)} - {f\left( {Z\text{/}Z_{n}} \right)}} \right\rbrack}}{where}} & {{EQUATION}\mspace{14mu} 13C} \\{{f(w)} = \begin{Bmatrix}{{(w)^{1/3}\mspace{14mu} {for}\mspace{14mu} w} > 0.008856} \\{{{7.787(w)} + {16\text{/}116\mspace{14mu} {for}\mspace{14mu} w}} \leq 0.008856}\end{Bmatrix}} & {{EQUATION}\mspace{14mu} 14}\end{matrix}$

The values of RGB_(i) and XYZ_(i) are the corresponding values ofdisplayed RGB and measured XYZ for each input value of RGB ranging from0 to 255. The summation performed above from i=0 to i=N−1 assumes a listor array of (N) XYZ measured values and their corresponding N values ofRGB used to generate the measured color. RGB=0 may be excluded in thiscalculation due to the possibility of a singularity or non-linearity ofbehavior in the region near or at RGB=0. In other words, not includingRGB=0 in this calculation can ensure that the overall RGB to XYZbehavior of the CRT behavior is being characterized without being skewedby behavior at or near RGB=0.

At this point, values for the parameters can be determined by errorminimization. For example, an error minimization can be performed on theadjustable parameter using any suitable error minimization method suchas a chi-squared method or a least squares fit. Other error minimizationtechniques could also be used. In any case, nominal initial values forthe chosen parameters can be used (in this case gamma=2.2 and blackonset=0.05). After the error minimization has been performed,calibration module 24 may check the average and maximum error betweenthe measured data and the analytical expected behavior that has beencalculated.

Experiments have shown that typical values for average error range from0.3-0.5 delta e and that maximum errors range from 0.6-1.0 delta e.These errors appear to be due to systematic quantization errors in theanalog-to-digital (A/D) and digital-to-analog (D/A) circuitry of thevideo card and the measurement device causing +/−shifts in XYZ as asmooth function of RGB. The sigma of noise of measurement for aparticular RGB color is typically quite low, e.g., 0.1 delta e. Thus, itshould be expected that this quantization error should be evenlydistributed about the perfect gamma curve behavior. Hence, although theaverage and maximum errors from analytical expected behavior may beslightly larger than variability due to random measurement noise, oneshould expect that the sum of the residuals should be close to zero.

To even further improve the error minimization, additional techniquescan be employed. For example, error minimization routines sometimes donot find the best minimum for the error function. In particular, a fatalerror can occur in the minimization routine, such as divergence ratherthan convergence. To avoid this, it may be advantageous to find the bestestimates possible for the initialization of the parameter values inorder to ensure that the error function is as near minimum as possibleto its ultimate value.

One way to pre-optimize the parameter values discussed above is tomeasure a smaller set of data for each channel separately and tocalculate and minimize the error of the separate channels first. In thisapproach, a smaller set of single channel data is obtained, i.e., theother two channels are set to 0 during data capture. Additionally, thepredictions for L*a*b* can be performed with two of the three RGB valuesset to 0. Finally, the error minimization can be performed only on theparameters for that one channel. These additional techniques can betterinsure that the error minimization technique will indeed identifyparameter values that minimize the error between analytical calculationsand measured behavior.

Once the parameter values of the device model are chosen (34),calibration module 24 can create and load entries into LUT 29 (35). Inthis manner, LUT 29 can adjust the image data to account for drift inCRT 12. LUT 29 may be in the form:

R′=f _(r) ⁻¹(R)=R _(o2)+(R _(max2) −R _(o2))R ^(γ) ^(r1) ^(/γ) ^(r2)  EQUATION 15A

G′=f _(g) ⁻¹(G)=G _(o2)+(G _(max2) −G _(o2))G ^(γ) ^(g1) ^(/γ) ^(g2)  EQUATION 15B

B′=f _(g) ⁻¹(B)=B _(o2)+(B _(max2) −B _(o2))B ^(γ) ^(b1) ^(/γ) ^(b2)  EQUATION 15C

B′=f _(g) ⁻¹(B)=B _(o2) +B _(max2) −B _(o2))B ^(γ) ^(b1) ^(/γ) ^(b2)  EQUATION 15C

At this point all the values of EQUATIONS 15A-15C are known with theexception of RGB_(max2). To obtain the values for RGB_(max2), theexpressions for the uncalibrated CRT indicated by EQUATIONS 7 and 8A-8Ccan be equated with the expression for the desired CRT behaviorindicated by EQUATION 6 after substituting R′G′B′ for RGB in theexpressions for the uncalibrated CRT. In other words, one can attempt topredict how close to the desired CRT behavior the uncalibrated CRT willbe after the adjustments stored in video LUT 29 are applied.Substituting these expressions for R′G′B′ into EQUATIONS 7 and 8A-8Cyields the following:

$\begin{matrix}{{\begin{pmatrix}\begin{matrix}X \\Y\end{matrix} \\Z\end{pmatrix} = {\begin{pmatrix}\begin{matrix}X_{dc} \\Y_{dc}\end{matrix} \\Z_{dc}\end{pmatrix} + {Y_{{wp}\; 2}{M\left( {X_{{wp}\; 2},Y_{wpz}} \right)}\begin{pmatrix}{f_{r\; 2}^{\prime}(R)} \\{f_{g\; 2}^{\prime}(G)} \\{f_{b\; 2}^{\prime}(B)}\end{pmatrix}}}}{where}} & {{EQUATION}\mspace{14mu} 16} \\{{f_{r\; 2}^{\prime}(R)} = {\left\lbrack \frac{\left( {R_{\max} - R_{o\; 2}} \right)}{\left( {1.0 - R_{o\; 2}} \right)} \right\rbrack^{\gamma_{r\; 2}}R^{\gamma_{r\; 1}}}} & {{EQUATION}\mspace{14mu} 17A} \\{{f_{g\; 2}^{\prime}(G)} = {\left\lbrack \frac{\left( {G_{\max} - G_{o\; 2}} \right)}{\left( {1.0 - G_{o\; 2}} \right)} \right\rbrack^{\gamma_{g\; 2}}G^{\gamma_{g\; 1}}}} & {{EQUATION}\mspace{14mu} 17B} \\{{f_{b\; 2}^{\prime}(B)} = {\left\lbrack \frac{\left( {B_{\max} - B_{o\; 2}} \right)}{\left( {1.0 - B_{o\; 2}} \right)} \right\rbrack^{\gamma_{b\; 2}}B^{\gamma_{b\; 1}}}} & {{EQUATION}\mspace{14mu} 17C}\end{matrix}$

Equating EQUATION 16 with EQUATION 6 for the ideal CRT behavior with agamma of 2.2, with additional measured dark current bias added yieldsthe following:

$\begin{matrix}{{\begin{pmatrix}X_{dc} \\Y_{dc} \\Z_{dc}\end{pmatrix} + {Y_{{wp}\; 1}{M\left( {X_{{wp}\; 1},Y_{{wp}\; 1}} \right)}\begin{pmatrix}{f_{r\; 1}(R)} \\{f_{g\; 1}(G)} \\{f_{b\; 1}(B)}\end{pmatrix}}} = {\begin{pmatrix}X_{dc} \\Y_{dc} \\Z_{dc}\end{pmatrix} + {Y_{{wp}\; 2}{M\left( {X_{{wp}\; 2},Y_{{wp}\; 2}} \right)}\begin{pmatrix}{f_{r\; 2}^{\prime}(R)} \\{f_{g\; 2}^{\prime}(G)} \\{f_{b\; 2}^{\prime}(B)}\end{pmatrix}}}} & {{EQUATION}\mspace{14mu} 18}\end{matrix}$

which can be reduced to:

$\begin{matrix}{\begin{pmatrix}{f_{r\; 2}^{\prime}(R)} \\{f_{g\; 2}^{\prime}(G)} \\{f_{b\; 2}^{\prime}(B)}\end{pmatrix} = {\frac{Y_{{wp}\; 1}}{Y_{{wp}\; 2}}{M^{- 1}\left( {x_{{wp}\; 2},y_{{wp}\; 2}} \right)}{M\left( {x_{{wp}\; 1},y_{{wp}\; 1}} \right)}\begin{pmatrix}{f_{r\; 1}(R)} \\{f_{g\; 1}(G)} \\{f_{b\; 1}(B)}\end{pmatrix}}} & {{EQUATION}\mspace{14mu} 19}\end{matrix}$

The product of the matrices is diagonal, and results in the followingthree equations:

$\begin{matrix}{{f_{r\; 2}^{\prime}(R)} = {\frac{Y_{{wp}\; 1}Y_{{wpR}\; 1}}{Y_{{wp}\; 2}Y_{{wpR}\; 2}}{f_{r\; 1}(R)}}} & {{EQUATION}\mspace{14mu} 20A} \\{{f_{g\; 2}^{\prime}(G)} = {\frac{Y_{{wp}\; 1}Y_{{wpG}\; 1}}{Y_{{wp}\; 2}Y_{{wpG}\; 2}}{f_{g\; 1}(G)}}} & {{EQUATION}\mspace{14mu} 20B} \\{{f_{b\; 2}^{\prime}(B)} = {\frac{Y_{{wp}\; 1}Y_{{wpB}\; 1}}{Y_{{wp}\; 2}Y_{{wpB}\; 2}}{f_{b\; 1}(B)}}} & {{EQUATION}\mspace{14mu} 20C}\end{matrix}$

RGB_(max2) can now be obtained by substituting EQUATIONS 2A-2C andEQUATIONS 17A-17C into EQUATIONS 20A-20C as follows:

$\begin{matrix}{{\left\lbrack \frac{\left( {R_{\max} - R_{o\; 2}} \right)}{\left( {1.0 - R_{o\; 2}} \right)} \right\rbrack^{\gamma_{r\; 2}}R^{\gamma_{r\; 1}}} = {\frac{Y_{{wp}\; 1}Y_{{wpR}\; 1}}{Y_{{wp}\; 2}Y_{{wpR}\; 2}}R^{\gamma_{r\; 1}}}} & {{EQUATION}\mspace{14mu} 21A} \\{{\left\lbrack \frac{\left( {G_{\max} - G_{o\; 2}} \right)}{\left( {1.0 - G_{o\; 2}} \right)} \right\rbrack^{\gamma_{g\; 2}}G^{\gamma_{g\; 1}}} = {\frac{Y_{{wp}\; 1}Y_{{wpG}\; 1}}{Y_{{wp}\; 2}Y_{{wpG}\; 2}}G^{\gamma_{g\; 1}}}} & {{EQUATION}\mspace{14mu} 21B} \\{{\left\lbrack \frac{\left( {B_{\max} - B_{o\; 2}} \right)}{\left( {1.0 - B_{o\; 2}} \right)} \right\rbrack^{\gamma_{b\; 2}}B^{\gamma_{b\; 1}}} = {\frac{Y_{{wp}\; 1}Y_{{wpB}\; 1}}{Y_{{wp}\; 2}Y_{{wpB}\; 2}}B^{\gamma_{b\; 1}}}} & {{EQUATION}\mspace{14mu} 21C}\end{matrix}$

and solving for R_(max), G_(max) and B_(max) as follows:

$\begin{matrix}{R_{\max} = {R_{o\; 2} + {\left( {1.0 - R_{o\; 2}} \right)\left\lbrack \frac{Y_{{wp}\; 1}Y_{{wpR}\; 1}}{Y_{{wp}\; 2}Y_{{wpR}\; 2}} \right\rbrack}^{1/\gamma_{r\; 2}}}} & {{EQUATION}\mspace{14mu} 22A} \\{G_{\max} = {G_{o\; 2} + {\left( {1.0 - G_{o\; 2}} \right)\left\lbrack \frac{Y_{{wp}\; 1}Y_{{wpG}\; 1}}{Y_{{wp}\; 2}Y_{{wpG}\; 2}} \right\rbrack}^{1/\gamma_{g\; 2}}}} & {{EQUATION}\mspace{14mu} 22B} \\{B_{\max} = {B_{o\; 2} + {\left( {1.0 - B_{o\; 2}} \right)\left\lbrack \frac{Y_{{wp}\; 1}Y_{{wp}\; B\; 1}}{Y_{{wp}\; 2}Y_{{wpB}\; 2}} \right\rbrack}^{1/\gamma_{b\; 2}}}} & {{EQUATION}\mspace{14mu} 22C}\end{matrix}$

The values of EQUATIONS 22A-22C can then be substituted into EQUATIONS15A-15C to define entries of LUT 29. The procedure can be repeated asdesired, each time using a correction function calculated from EQUATIONS15A-15C from a previous iteration. In other words, the adjustments maybe implemented as a closed loop such that the adjustment values continueto converge with each iteration of the procedure. The procedure may berepeated each day, or may be repeated as desired. For example, it may beadvantageous to repeat the procedure before a contract proof image isdisplayed. Experiments have shown that typically CRTs drift by only 1.0to 3.0 delta e over an approximately 24 hour period. Thus, for manyapplications one calibration per day is adequate.

Additionally, intelligent process control may be implemented such as bycalculating the adjustment to the most recent correction function, andperforming a partial correction rather than a full correction. Thedegree of correction can be adjusted based on the noise of measurementversus the magnitude of error. Additionally, intelligent process controlmay be implemented by correcting the most recent correction functiononly when the average and/or max errors exceed a predetermined value.

The target Yxy value for white can often be achieved to an acceptablelevel by automatic adjustment of the CRT analog parameters as describedabove. Optionally, calibration module 24 can adjust the gammas andonsets without modifying the maximum correction values. Likewise, theonsets may be minimized by optimization of the CRT analog parameters. Inthat case, the calibration method described above can be used to adjustonly the gamma behavior of the RGB channels in order to ensure accurategray balance. Even if adjustments are performed on each RGB channel forthe black onset, gamma, and max value, minimizing the magnitude ofadjustment for black onset and max value is desirable when limited by an8 bit video LUT in order to minimize the effects of quantization.

If the choice of the analog settings is used to calibrate the systemwhite point to a value close to the target white value (for example, nomore than +/−0.5 delta e error in L*,a*, or b* using the target whitevalue for X_(a)Y_(a)Z_(a)) there are at least two ways to characterizethe gamma curve properties of the system. One way is to set the whitepoint of the analytical model to the value of the measured white pointof the CRT system after the analog selections are performed. This shouldresult in a gamma curve for R, G, and B that is minimized in error withrespect to a “relative white” reference target. In other words a targetthat is exactly 2.2 gamma for R, G, and B, and a white point equal tothe current white of the CRT, i.e. close to but not exactly the targetvalue of Yxy.

Another way to characterize the gamma curve properties of the system isto set the white point of the analytical model to the desired targetvalue for the white point of the CRT system. When this approach is used,the calculated model will always have a small error relative to themeasured data at RGB=white. However, the overall model may obtain abetter fit to the measured data. This means that small errors in thewhite point will to some extent be accounted for in the overallanalytical model for the CRT. Consequently, the final gamma curve of thesystem should have a minimized error with respect to the “absolutewhite” reference target, i.e. the reference target that has 2.2 gammafor R, G, and B as well as a white point that is that desired targetwhite point of the system.

Additional setting conditions for the CRT that can be checked beforeperforming the above procedure may include the following. First, allvalues of the RGB reference space in the equations above should be lessthan or equal to the values of Y for the physical RGB device beingcalibrated. Second, all values of the RGB onsets for the physical RGBdevice being calibrated should be in a “correctable” range. For example,with current CRT technology, an estimate for what constitutes“correctable” is 5<RGB_(o2)<40 in units of gray levels. Third, allvalues of XYZ_(dc) should be positive to avoid clipping, and should bebelow a “reasonable” level. In that case, with current CRT technology,an estimate of what constitutes “reasonable” is 0.5 candelas/meter²

The first setting condition simply means that the maximum values of RGBcan always be reduced in order to hit the target, but cannot exceed 100%if the intensity of a channel is too low. The second setting conditioncan ensure that a good black (RGB=0) can always be achieved. The thirdsetting condition can ensure that the measurement device, plus straylight, plus CRT minimum luminance values are reasonably low. The CRTwhite point should be set close to the vicinity of the targeted whitepoint such that the maximum adjustment values of RGB for the RGB devicebeing calibrated are in the range of 0.95<RGBmax<1.0.

FIG. 5 is another flow diagram illustrating a more general overview of acalibration technique for a CRT according to the invention. As shown,the output of a CRT is measured (51). Then, parameter values of a devicemodel of the CRT are chosen (52). Finally, image data is adjusted sothat output of the CRT is more accurate (53).

In particular, when the number of adjustable parameters of the CRT isestablished to be less than a number of measurements of the CRT,calibration can be improved. For example, rather than adjusting eachinput of the CRT according to a measured output, i.e., a one-to-onemapping of input and measurements, the invention establishes a devicemodel that has fewer adjustable parameters than the number of measuredoutputs. In this manner, the technique does not overcompensate formeasured results that have errors which would be expected. In otherwords, even after applying an adjustment to input at the video card, oneshould expect small errors due to factors unrelated to drift in the CRTsuch as, inaccuracies in the measuring device. The invention can achievea balance between analytical modeling and empirical measurement so asnot to overcompensate for measured errors that should be expected.

By using the techniques described herein, improved calibration of CRTscan be achieved. Specifically, the techniques described herein can beused to calibrate a display such that an average color error isapproximately less than (0.75 delta e) from an analytical expected coloroutput and a maximum color error is approximately less than (1.5 deltae) from the analytical expected color output. More specifically, averagecolor errors approximately between (0.3 delta e) and (0.75 delta e) fromthe analytical expected color output and maximum color errorsapproximately between (0.6 delta e) and (1.1 delta e) from theanalytical expected color output can be achieved. Even morespecifically, average color errors approximately between (0.3 delta e)and (0.4 delta e) from the analytical expected color output and themaximum color errors approximately between (0.6 delta e) and (0.8 deltae) from the analytical expected color output have been achieved. Averagecolors errors approaching (0.3 delta e) approach the theoretical limitsthat can be achieved with the current state of CRTs and measurementdevices. As measurement devices and CRTs improve, however, thetheoretical limits of calibration may also improve. In that case, theinvention may achieve even better average errors and maximum colorerrors.

FIG. 6 is another flow diagram generally illustrating a calibrationtechnique that can be applied to any imaging device in accordance withthe invention. As shown, the imaging device is characterized with adevice model (61). In that case, an average error between an analyticalexpected value of the device model and a measured output of a subset ofdevice values of the imaging device can be made on the order of anexpected error. Furthermore, in some cases, a subset of device valuessubstantially corresponding to neutral colors may comprise the output.After characterizing the imaging device (61), the image rendering by theimaging device can be adjusted to achieve a target behavior (62). Forexample, adjusting the imaging rendering comprises adjusting image data,such as by using a LUT or by creating dynamic color profiles asdescribed below with reference to FIG. 7.

The device model used to characterize the imaging device may include oneor more adjustable parameters. In that case, the technique may furtherinclude choosing adjustable parameters of the device model. Also, thenumber of chosen parameters may be less than a number of measurementsused to define the measured output of the imaging device. For example,in the case where the imaging device is a CRT, the adjustable parametersmay comprise a gamma value and a black onset value. However, in otherexamples, the parameters can be specified according to the imagingdevice.

FIG. 7 illustrates an alternative embodiment of imaging station 10. Inthis case, the same calibration techniques can be used to calibrate CRT12. However, rather than apply the adjustments using a LUT, thisembodiment creates dynamic color profiles in color matching module (CMM)72. In other words, after calibration module 74 has performed thecalibration procedure, the adjustment to account for drift in CRT 12 canbe applied by generating dynamic color profiles in CMM 72. The colorprofiles are dynamic in the sense that they change with calibrationmeasurements. Thus, as CRT 12 drifts, the dynamic color profile maychange. In other words, CMM 72 may receive the calibration data fromcalibration module 74 and can incorporate the calibration informationinto a device profile specifically for CRT 12. Adjusted image data canthen be fed through display driver 75 and video card 76, possiblywithout any additional adjustments. In this manner, the need for a LUTwithin video card 76 can be avoided.

FIG. 8 illustrates a soft proofing system 80. Soft proofing system 80may implement one or more aspects of the invention to realize accuratecolor rendering and color matching in a soft proofing process. Softproofing system 80 includes an administrative computer 82.Administrative computer 82 can be thought of as a server computer forsoft proofing system 82. Administrative computer 82 may serve up imagesto imaging stations 10A-10D (hereafter imaging stations 10). Colorspecialists at imaging stations 10 can inspect the images, and possiblyprovide feedback by marking or highlighting the images and retuningmarked-up copies to administrative computer 82. Upon receiving feedback,an administrator may implement changes to the image using administrativecomputer 82. Once the administrator and the reviewers associated withimaging stations 10 reach agreement on the appearance of the colorimage, the image can be printed via a printing press or another highquality printing device.

Administrative computer 82 may be directly coupled to the variousimaging stations 10, possibly forming a local area network (LAN), oralternatively, the administrative computer 82 may be coupled to imagingstations 10 via a wide area network or a global network 84 such as theInternet. The various imaging stations 10 may implement one or more ofthe calibration techniques described above in order to improvecalibration and thus improve color accuracy. Indeed, improvedcalibration can directly impact the ability to achieve an effective softproofing system 80 or another color intensive application.

In one embodiment, imaging stations 10 collectively define a set ofCRTs, with each CRT in the set being calibrated to within the errorsoutlined above. In that case, it can be ensured that drift in the CRT ofany imaging station 10 is adequately accounted for, such that contractproof color quality can be rendered at each imaging station 10.

Many aspects of the invention have been described as being at leastpartially implemented in software. Alternatively, exemplary hardwareimplementations may include implementations within a DSP, an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a programmable logic device, specifically designed hardwarecomponents, or any combination thereof.

Although many aspects of the invention have been described in thecontext of a calibration technique for calibrating a display device inthe form of a cathode ray tube, aspects of the invention may be readilyapplicable to calibration of other imaging devices, including liquidcrystal displays, plasma displays, various printing devices, or otherimaging devices such as scanner or the like. Accordingly, otherimplementations and embodiments are within the scope of the followingclaims.

1. A method for calibrating a display device, the method comprising:identifying a plurality of device-dependent color coordinates intendedto produce a plurality of neutral colors; reproducing a plurality ofcolors on the display device for the plurality of device-independentcolor coordinates; measuring a plurality of device-independent colorcoordinates for the plurality of reproduced colors; identifying aplurality of target device-independent color coordinates correspondingto at least some of the plurality of neutral colors; and determiningadjustments to a device-dependent color coordinate LUT wherein theadjustments are designed to reduce differences between the measured andtarget device-independent coordinates to within a range of expectedmeasurement errors and wherein determining comprises determining withoutmeasuring additional color coordinates.
 2. A method according to claim 1wherein the range of expected measurement errors comprise an averagecolor error of 0.3 delta E or less.
 3. A method according to claim 1wherein the range of expected measurement errors comprise a maximumcolor error of 0.6 delta E or less.
 4. A method according to claim 1wherein identifying the plurality of target device-independent colorcoordinates corresponding to the plurality of neutral colors comprises:characterizing the display device with a device model; and identifyingcoordinates using the device model.
 5. A method according to claim 4wherein characterizing the display device with a device model comprisesdetermining values for a plurality of adjustable parameters of thedevice model.
 6. A method according to claim 1 wherein identifying theplurality of target device-independent color coordinates correspondingto the plurality of neutral colors comprises pre-determined colorcoordinates.
 7. A computer readable medium storing program code thatwhen executed calibrates a display device by: identifying a plurality ofdevice-dependent color coordinates intended to produce a plurality ofneutral colors; reproducing a plurality of colors on the display devicefor the plurality of device-independent color coordinates; measuring aplurality of device-independent color coordinates for the plurality ofreproduced colors; identifying a plurality of target device-independentcolor coordinates corresponding to at least some of the plurality ofneutral colors; and determining adjustments to a device-dependent colorcoordinate LUT wherein the adjustments are designed to reducedifferences between the measured and target device-independentcoordinates to within a range of expected measurement errors and whereindetermining comprises determining without measuring additional colorcoordinates.
 8. A computerized apparatus for calibrating a displaydevice, comprising: a measurement device operative to supply adevice-independent color coordinate for a color reproduced by thedisplay device; and a calibration module operative to: identify aplurality of device-dependent color coordinates intended to produce aplurality of neutral colors; supply the plurality of device-dependentcolor coordinates to the display device to display the intended neutralcolors; obtain device-independent color coordinates for the intendedneutral colors from the measurement device; identify a plurality oftarget device-independent color coordinates corresponding to at leastsome of the intended neutral colors; and determine adjustments to adevice-dependent color coordinate LUT wherein the adjustments aredesigned to reduce the difference between the measured and targetdevice-independent coordinates to within the range of expectedmeasurement errors and wherein the calibration module determinesadjustments without measuring additional color coordinates.